外观
论文arXiv 2610.0842122 天前
通过夹爪内物体重定向实现机器人插入的抓取后运动学修复
针对抓取后插入路径可能运动学不可行的问题，提出夹爪内物体重定向修复，并在UR5e仿真和真机实验验证。
Universal Robots UR5e原文链接：Post-Grasp Kinematic Repair for Robotic Insertion via Object-in-Gripper Reorientation（arXiv 2610.08421 · 发布日期：2026-10-06）
Post-Grasp Kinematic Repair for Robotic Insertion via Object-in-Gripper Reorientation
针对抓取后插入路径可能运动学不可行的问题，提出夹爪内物体重定向修复，并在UR5e仿真和真机实验验证。
核心要点
- 稳定抓取仍可能因夹爪内物体位姿使机器人沿插入路径接近奇异或关节限位。
- 方法在给定抓取和固定插入路径下搜索小幅重定向，用分支感知IK图评估可行裕度。
- 学习的任务条件先验用于改进候选查询顺序，所选重定向通过触觉外在操作执行。
- UR5e仿真中平均重定向降51.5%，加入学习排序后规划时间再降51.1%。
验证范围与局限：验证范围包括UR5e仿真和真实机器人实验，任务为固定插入路径下的抓取后重定向修复。
英文摘要 (Abstract)
A stable grasp does not guarantee kinematically feasible robotic insertion because the object-in-gripper transform may force the robot towards singularities or joint limits along the prescribed insertion path. We study post-grasp kinematic feasibility repair through object-in-gripper reorientation. Given an achieved grasp and a fixed insertion path, we seek a small reorientation that restores kinematic feasibility. Sequential IK can miss such candidates by following an unfavorable joint-space path, while the nonsmooth feasibility landscape makes the search computationally expensive. We evaluate candidates using a branch-aware IK graph that maximizes the minimum feasibility margin over the discretized insertion path and use a learned task-conditioned prior to improve query ordering. The selected reorientation is executed through tactile-based extrinsic manipulation. In UR5e simulations, the planner without learned ranking reduces mean reorientation over successful trials by 51.5% compared with grid-based sequential IK. Adding learned ranking reduces this planner's mean planning time by an additional 51.1%. Real-robot experiments validate the complete pipeline.
作者：Haegu Lee, Christoffer Sloth
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